One third of patients with medically refractory focal epilepsy have normal-appearing MRI scans. This poses a problem as identification of the epileptogenic region is required for surgical treatment. This study performs a multimodal voxel-based analysis (VBA) to identify brain abnormalities in MRI-negative focal epilepsy. Data was collected from 69 focal epilepsy patients (42 with discrete lesions on MRI scans, 27 with no visible findings on scans), and 62 healthy controls. MR images comprised T1-weighted, fluid-attenuated inversion recovery (FLAIR), fractional anisotropy (FA) and mean diffusivity (MD) from diffusion tensor imaging, and neurite density index (NDI) from neurite orientation dispersion and density imaging. These multimodal images were coregistered to T1-weighted scans, normalized to a standard space, and smoothed with 8 mm FWHM. Initial analysis performed voxel-wise one-tailed t-tests separately on grey matter concentration (GMC), FLAIR, FA, MD, and NDI, comparing patients with epilepsy to controls. A multimodal non-parametric combination (NPC) analysis was also performed simultaneously on FLAIR, FA, MD, and NDI. Resulting p-maps were family-wise error rate corrected, threshold-free cluster enhanced, and thresholded at p < 0.05. Sensitivity was established through visual comparison of results to manually drawn lesion masks or seizure onset zone (SOZ) from stereoelectroencephalography. A leave-one-out cross-validation with the same analysis protocols was performed on controls to determine specificity. NDI was the best performing individual modality, detecting focal abnormalities in 38% of patients with normal MRI and conclusive SOZ. GMC demonstrated the lowest sensitivity at 19%. NPC provided superior performance to univariate analyses with 50% sensitivity. Specificity in controls ranged between 96 and 100% for all analyses. This study demonstrated the utility of a multimodal VBA utilizing NPC for detecting epileptogenic lesions in MRI-negative focal epilepsy. Future work will apply this approach to datasets from other centres and will experiment with different combinations of MR sequences.

三分之一的药物难治性局灶性癫痫患者的 MRI 扫描结果正常。这带来了一个问题，因为手术治疗需要识别致癫痫区域。本研究执行基于多模态体素的分析 (VBA)，以识别 MRI 阴性局灶性癫痫的大脑异常。数据收集自 69 名局灶性癫痫患者（42 名 MRI 扫描有离散病灶，27 名扫描没有可见发现）和 62 名健康对照。MR 图像包括来自扩散张量成像的 T1 加权流体衰减反转恢复 (FLAIR)、分数各向异性 (FA) 和平均扩散率 (MD)，以及来自轴突取向分散和密度成像的轴突密度指数 (NDI)。这些多模态图像与 T1 加权扫描进行配准，标准化为标准空间，并使用 8 mm FWHM 进行平滑处理。初始分析分别对灰质浓度 (GMC)、FLAIR、FA、MD 和 NDI 进行体素单尾 t 检验，将癫痫患者与对照组进行比较。还同时对 FLAIR、FA、MD 和 NDI 进行了多模态非参数组合 (NPC) 分析。生成的 p-map 进行了家庭错误率校正，无阈值聚类增强，并在 p < 0.05 时设置了阈值。通过视觉比较结果与立体脑电图手动绘制的病变掩膜或癫痫发作起始区 (SOZ) 建立敏感性。对对照进行具有相同分析方案的留一法交叉验证以确定特异性。NDI 是表现最好的个体化方式，在 38% 的 MRI 正常和结论性 SOZ 患者中检测到局灶性异常。GMC 的灵敏度最低，为 19%。NPC 以 50% 的灵敏度为单变量分析提供了卓越的性能。对于所有分析，对照的特异性介于 96% 和 100% 之间。这项研究证明了利用 NPC 的多模式 VBA 用于检测 MRI 阴性局灶性癫痫中的致癫痫病灶的效用。未来的工作将把这种方法应用于其他中心的数据集，并将试验不同的 MR 序列组合。